Agenda

• Why Modelling?
• Microsimulation
• Lifetable Modelling
• Summary

Why Modelling

• Simple representation of complexity
• Experiments are not possible or feasible (e.g. randomised control trials)
• Explore alternative scenarios consequences
• Trials are short (intermediate end-points), need for extrapolation Cholesterol levels (trial) to heart disease (model)
• Generalisable to other settings (age groups, country)
• Synthesizing data from various sources
• Informing decisions in the absence of hard data (Covid!)

Example - Transport PM 2.5 for Munich

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How it differs

• Microsimulation models operate at the level of individual units such as persons, households, vehicles, or firms, whereas aggregate models represent collective properties
• Each unit in microsimulation models is treated as an autonomous entity, and the interaction of the units is allowed to vary depending on stochastic (randomized) parameters that represent individual preferences and tendencies
• Microsimulation models simulate large representative populations of these low-level entities to draw conclusions that apply to higher levels of aggregation such as an entire country
• Microsimulation models are used to estimate how demographic, behavioral, and policy changes might affect individual outcomes and to better understand the effects of current policies
• Microsimulation models are explorative tools that can be used for explanation or prediction
• Microsimulation models can simulate the effects of very fine-grained as well as broader policy changes3
• Microsimulation models can be resource-intensive to develop and apply, and difficult to understand and evaluate because they must usually meld together a variety of data and research results of varying degrees of quality and, in the process, make many unsupported assumptions .

In summary, microsimulation is a unique modeling technique that allows for the simulation of individual units and their interactions, providing a more detailed and nuanced understanding of the effects of policies and other changes.

Tutorial paper

Krijkamp, Eline M., et al. “Microsimulation modeling for health decision sciences using R: a tutorial.” Medical Decision Making 38.3 (2018): 400-422

Definition - Lifetable Modelling

• Two states model: alive and dead
• Outcomes: life years, and life expectancy.

I/O

• Inputs: Mortality rates
• Depicts: Life expectancy at different ages
• Period life tables: individuals exposed over hypothetical life course to mortality rates observed in one calendar year
• Projected mortality rates: Simulation relies on cohort life tables

Source of Inputs

• All-cause mortality rate: National Statistics offices/Global Burden of Disease Study

• All-cause mortality rate trends: National Statistics offices

• Population for each cohort (can also be 100,000 or similar figure)

Two-state lifetable model (1/3)

Two-state lifetable model (2/3)

Two-state lifetable model (3/3)

• Average life years without intervention: 6,362

• Average life years without intervention: 7,005

• Life years gained (undiscounted): 643 (7,005 - 6,362)

• Caveat: Probability of dying increases with age

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